RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments
arXiv:2606.15862v1 Announce Type: new Abstract: Large language model (LLM) agents have made rapid progress on short-horizon, well-scoped tasks, yet their ability to sustain coherent decisions in dynamic long-horizon environments remains uncertain. We introduce RetailBench, a data-grounded simulation benchmark for evaluating tool-using LLM agents in single-store supermarket operation. RetailBench models retail management as a partially observable decision process and is designed to support thousand-day-scale simulations. In this environment, agents must manage pricing, replenishment, supplier s
The rapid advancement in LLM capabilities has created an urgent need for robust evaluation benchmarks to assess their performance in complex, long-horizon decision-making scenarios.
This benchmark addresses a critical gap in evaluating AI agent coherence and sustained decision-making, which is essential for deploying LLMs in real-world operational environments.
The introduction of RetailBench provides a standardized, data-grounded simulation environment, shifting LLM agent evaluation from short, simple tasks to complex, long-duration operational challenges.
- · LLM researchers
- · AI agent developers
- · Retail sector
- · Simulation platforms
- · Companies relying on simplistic LLM evaluations
- · Traditional retail management software
RetailBench will enable more accurate and rigorous testing of LLM agents' ability to handle dynamic, long-horizon tasks.
Improved LLM agents developed through such benchmarks could automate complex operational roles in retail and other industries, leading to significant efficiency gains.
The success of LLM agents in simulated retail environments could accelerate their deployment into other complex, real-world sectors, transforming white-collar and operational workflows.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI